Over the last decades, we have witnessed a tremendous increase in the utilization and availability of wireless networks and devices. This growth is largely founded by the introduction of mobile devices and the Internet of Things paradigm. In parallel, the demands and expectations of users, in terms of connectivity, bandwidth, quality, and services, have never been higher. These evolutions have led to a complex and heterogeneous situation where different devices, applications, and technologies consist next to each other at identical or overlapping physical locations, often competing for the same wireless resources. Existing management approaches typically operate in a static manner as no coordination exists across different communication technologies and devices. Despite being equipped with multiple network interfaces, modern devices tend to statically select one of the available technologies (e.g., Wi-Fi or LTE) or connection points (e.g., access points or base stations) based on predefined priorities. As such, decisions are left to the applications or, even worse, the user and it is impossible to automatically react to the inevitable dynamic disruptions or changes in the wireless context. Furthermore, different technologies tend to compete against each other in the same frequency bands.

In contrast, in this dissertation, we try to tackle the aforementioned challenges in a more transparent and fundamental manner. The main idea is that a user is only aware of the fact that its device is connected to the Internet, while the network takes care of all the underlying decision-making and the provisioning of resources. To this extent, we present the ORCHESTRA framework for seamless inter-technology network management. The framework consists of two components: a centralized controller that maintains a global network view and a virtual MAC layer that offers a single connection point to the upper layers, while transparently bonding over the underlying network technologies. As such, it introduces packet-level inter-technology handovers, load balancing, and duplication across the different underlying links by using packet matching rules. On top of the introduced ORCHESTRA framework, intelligence is needed that can utilize the features of the framework to actually optimize the network. To this extent, we present a series of algorithms that significantly increase the network-wide throughput based purely on real-time monitoring information. Finally, we explore the option of detecting traffic patterns in the wireless spectrum. This information can be used by management algorithms to further optimize the network, especially in areas with multiple overlapping networks.